Taskforce 3 Data Analysis Subgroup Report
Laetitia, Giulia, John, Winfred
Data analysis approach
This subgroup of ERC Taskforce 3 (Training) analysed and interpreted results of the student and programme staff surveys. When data was made available, analysts downloaded anonymised data to their local computers (students survey) from the OneDrive folder it was shared through, and kept identifiable data in the folder (programme staff survey).
All four analysts were assigned a number of questions to analyse. Analyses were then collated. All analyses were conducted using R version 4.1.0 and RStudio Build 554.
For quantitative questions, where responses were multiple choice, data was tallied and plotted with bar charts, subsetting the results by the year of study of the respondent, using package ‘ggplot2’ version 3.3.6. Open questions were read by the assigned analyst. Key themes were extracted, and data was reassigned to newly defined categories and plotted with bar charts. The code used is shared below for transparency (toggle ‘code’).
Student survey
# Loading necessary packages
library(readxl)
library(ggplot2)
library(dplyr)
library(tidyr)# Loading the data
student_survey_responses <- as.data.frame(read_excel("Student survey-task-force-3.xlsx"))student_survey_responses_q1_clean <-
na.omit(data.frame("Year" = as.factor(student_survey_responses[, 6])))
n_q1 <-
length(student_survey_responses_q1_clean[, 1]) #get the sample size for Q1
plot_q1 <- ggplot(data = student_survey_responses_q1_clean,
mapping = aes (x = Year, fill = Year)) +
geom_bar(color = "black") + #barcharts
labs(
title = " Which year of your PhD are you in (n = 36) ?",
x = "Answer",
y = "Count",
color = "Answer"
) +
theme_classic() + theme(legend.position = "none") + scale_fill_brewer(palette = "Accent") +
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,18)
q1_table = as.data.frame(table(student_survey_responses_q1_clean))
names(q1_table) = c('Year', 'Count')
plot_q1
Awareness of training
Key points:
Most people reported being informed of training possibilities across all years
Most students reported knowing where to find information about training
Most students said registering for training was easy
Students reported Wellcome made them aware of possible training in comparable numbers (although more indicated they Wellcome did not); a difference can be seen in the Year 3 group, who indicated largely being informed of Wellcome training.
student_survey_responses_q3_clean <-
na.omit(data.frame(
"Year" = as.factor(student_survey_responses[, 6]),
"Q3" = as.factor(student_survey_responses[, 8])
)) #create a dataframe with Q1 and Q3 and remove NA values in answers to Q3
n_q3 <-
length(student_survey_responses_q3_clean[, 1]) #get the sample size for Q3
student_survey_responses_q3_clean$Q3 <-
factor(
student_survey_responses_q3_clean$Q3,
levels = c("Strongly Agree", "Agree", "Neutral")
) #reorder the levels of Q3 in the way we want to present them
plot_q3_full <- ggplot(data = student_survey_responses_q3_clean,
mapping = aes (x = Q3, fill = Q3)) +
geom_bar(color = "black") +
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none", axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,20)
plot_q3 <- ggplot(data = student_survey_responses_q3_clean,
mapping = aes (x = Q3, fill = Q3)) +
geom_bar(color = "black") + #barcharts
facet_wrap( ~ Year) + # create a different plot for each Year
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none", axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,10)
q3_table = student_survey_responses_q3_clean %>% group_by(Year, Q3) %>% summarise(count =
n())
gridExtra::grid.arrange(plot_q3_full,
plot_q3,
ncol = 2,
top = "I was informed about upcoming training possibilities (n = 32)")
student_survey_responses_q2_clean <-
na.omit(data.frame(
"Year" = as.factor(student_survey_responses[, 6]),
"Q2" = as.factor(student_survey_responses[, 7])
)) #create a dataframe with Q1 and Q2 and remove NA values in answers to Q2
n_q2 <-
length(student_survey_responses_q2_clean[, 1]) #get the sample size for Q2
student_survey_responses_q2_clean$Q2 <-
factor(student_survey_responses_q2_clean$Q2, levels = c("Yes", "No")) #reorder the levels of Q2 in the way we want to present them "
plot_q2_full <- ggplot(data = student_survey_responses_q2_clean,
mapping = aes (x = Q2, fill = Q2)) +
geom_bar(color = "black") +
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,32)
plot_q2 <- ggplot(data = student_survey_responses_q2_clean,
mapping = aes (x = Q2, fill = Q2)) +
geom_bar(color = "black") + #barcharts
facet_wrap( ~ Year) + # create a different plot for each Year
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,15)
q2_table = student_survey_responses_q2_clean %>% group_by(Year, Q2) %>% summarise(count =
n())
gridExtra::grid.arrange(plot_q2_full,
plot_q2,
ncol = 2,
top = "I know where to find information about the training offered (n = 36)")
student_survey_responses_q4_clean <-
na.omit(data.frame(
"Year" = as.factor(student_survey_responses[, 6]),
"Q4" = as.factor(student_survey_responses[, 9])
)) #create a dataframe with Q1 and Q4 and remove NA values in answers to Q4
n_q4 <-
length(student_survey_responses_q4_clean[, 1]) #get the sample size for Q4
student_survey_responses_q4_clean$Q4 <-
factor(student_survey_responses_q4_clean$Q4, levels = c("Yes", "No")) #reorder the levels of Q4 in the way we want to present them "
plot_q4_full <- ggplot(data = student_survey_responses_q4_clean,
mapping = aes (x = Q4, fill = Q4)) +
geom_bar(color = "black") +
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,32)
plot_q4 <- ggplot(data = student_survey_responses_q4_clean,
mapping = aes (x = Q4, fill = Q4)) +
geom_bar(color = "black") + #barcharts
facet_wrap( ~ Year) + # create a different plot for each Year
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,13)
q4_table = student_survey_responses_q4_clean %>% group_by(Year, Q4) %>% summarise(count =
n())
gridExtra::grid.arrange(plot_q4_full,
plot_q4,
ncol = 2,
top = "It is easy to register to attend training (n = 34)")
student_survey_responses_q5_clean <-
na.omit(data.frame(
"Year" = as.factor(student_survey_responses[, 6]),
"Q5" = as.factor(student_survey_responses[, 10])
)) #create a dataframe with Q1 and Q5 and remove NA values in answers to Q5
n_q5 <-
length(student_survey_responses_q5_clean[, 1]) #get the sample size for Q5
student_survey_responses_q5_clean$Q5 <-
factor(student_survey_responses_q5_clean$Q5, levels = c("Yes", "No")) #reorder the levels of Q5 in the way we want to present them "
plot_q5_full <- ggplot(data = student_survey_responses_q5_clean,
mapping = aes (x = Q5, fill = Q5)) +
geom_bar(color = "black") +
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,20)
plot_q5 <- ggplot(data = student_survey_responses_q5_clean,
mapping = aes (x = Q5, fill = Q5)) +
geom_bar(color = "black") + #barcharts
facet_wrap( ~ Year) + # create a different plot for each Year
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,11)
q5_table = student_survey_responses_q5_clean %>% group_by(Year, Q5) %>% summarise(count =
n())
gridExtra::grid.arrange(plot_q5_full,
plot_q5,
ncol = 2,
top = "My Wellcome trust programme made me aware of training opportunities (n = 36)")
Scheduling of training
Key points:
Most people across all years agreed that training was provided at an appropriate time
Students reported that training was not generally repeated to account for scheduling conflicts
student_survey_responses_q6_clean <-
na.omit(data.frame(
"Year" = as.factor(student_survey_responses[, 6]),
"Q6" = as.factor(student_survey_responses[, 11])
)) #create a dataframe with Q1 and Q6 and remove NA values in answers to Q6
n_q6 <-
length(student_survey_responses_q6_clean[, 1]) #get the sample size for Q6
student_survey_responses_q6_clean$Q6 <-
factor(
student_survey_responses_q6_clean$Q6,
levels = c(
"Strongly agree",
"Agree",
"Disagree",
"Strongly disagree",
"Not applicable"
)
) #reorder the levels of Q6 in the way we want to present them
plot_q6_full <- ggplot(data = student_survey_responses_q6_clean,
mapping = aes (x = Q6, fill = Q6)) +
geom_bar(color = "black") +
labs(x = "Answer", y = "Count", fill = "Answer") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,22)
plot_q6 <- ggplot(data = student_survey_responses_q6_clean,
mapping = aes (x = Q6, fill = Q6)) +
geom_bar(color = "black") + #barcharts
facet_wrap( ~ Year) +
labs(x = "Answer", y = "Count", fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,10)
q6_table = student_survey_responses_q6_clean %>% group_by(Year, Q6) %>% summarise(count =
n())
gridExtra::grid.arrange(plot_q6_full,
plot_q6,
ncol = 2,
top = "The training is delivered at an appropriate time to provide me \n with the skills needed for my PhD project (n = 35)")
student_survey_responses_q7_clean <-
na.omit(data.frame(
"Year" = as.factor(student_survey_responses[, 6]),
"Q7" = as.factor(student_survey_responses[, 12])
)) #create a dataframe with Q1 and Q7 and remove NA values in answers to Q7
n_q7 <-
length(student_survey_responses_q7_clean[, 1]) #get the sample size for Q7
student_survey_responses_q7_clean$Q7 <-
factor(student_survey_responses_q7_clean$Q7, levels = c("Yes", "No")) #reorder the levels of Q7 in the way we want to present them "
plot_q7_full <- ggplot(data = student_survey_responses_q7_clean,
mapping = aes (x = Q7, fill = Q7)) +
geom_bar(color= "black") +
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,22)
plot_q7 <- ggplot(data = student_survey_responses_q7_clean,
mapping = aes (x = Q7, fill = Q7)) +
geom_bar(color= "black") + #barcharts
facet_wrap( ~ Year) + # create a different plot for each Year
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,12)
q7_table = student_survey_responses_q7_clean %>% group_by(Year, Q7) %>% summarise(count =
n())
gridExtra::grid.arrange(plot_q7_full,
plot_q7,
ncol = 2,
top = "Training was repeated to account for scheduling conflict (n = 33)")
Content of training
Key points:
Most students did not receive training on equality and diversity; Those who did found it somewhat useful acaross years.
Most students found training in professional, personal, wellbeing, engagement and career development useful, though many did not receive training in these areas.
Technical training and cohort-specific training were considered useful or extremely useful across years.
dat_studentupdated <- student_survey_responses %>% rename('yearofstudy'=6,'trainingareas'=13,'equal_diversity'=14,'professional_dev'=15,'personal_dev'=16,'wellbeing'=17,'career_dev'=18,'tech_training'=19,'cohort_training'=20,'engage_outreach'=21,'usefulnessoftraining'=22,'trainingformat'=25) # reframe to make things easierstudent_survey_responses_q8b_clean <- na.omit(data.frame("Year" = as.factor(student_survey_responses[,6]),
"Q8b" = as.factor(student_survey_responses[,13])))
mylist = unlist(sapply(as.character(student_survey_responses_q8b_clean[,2]), strsplit,split =';', USE.NAMES = F)) # get all responses
Val =c(levels(as.factor(mylist))) # get unique responses
Q8b = student_survey_responses_q8b_clean %>% separate(Q8b, into = paste0("V", 1:8), sep = ";") # separate column of responses
Q8b[Q8b == ""] <- NA # set empty values as NA
# wide to long
qwide = gather(Q8b, V, Answer, V1:V8)
qwide$Answer[qwide$Answer == "participatory research "] <- NA
qfinal = subset(qwide, !is.na(qwide$Answer))
p1 = ggplot(qfinal, mapping = aes(x = Answer, fill = Answer)) + geom_bar(color="black") + # create a different plot for each Year
labs(title = "Overall", y = "Count", fill = "Answer") + scale_fill_brewer(palette = "Set3") + theme_classic() + theme(legend.position = "none", axis.title.x = element_blank(), axis.text.x = element_blank(), title=element_text(size=8)) +
geom_text(stat='count', aes(label=..count..), position = position_stack(vjust = 0.5), size = 2)
p2 = ggplot(qfinal, mapping = aes(x = as.factor(Answer), fill = as.factor(Answer))) + geom_bar(color="black") + facet_wrap(~ Year) + # create a different plot for each Year
labs( y = "Count", fill = "Answer") + theme_classic() + theme(legend.position = "right", axis.title.x = element_blank(),
axis.text.x = element_blank(),legend.text = element_text(size=7))+
geom_text(stat='count', aes(label=..count..), position = position_stack(vjust = 0.5), size = 2)+ scale_fill_brewer( palette = "Set3", labels=c("Career Development",
"Cohort Specifc Training",
"Engagement and Outreach",
"Equality and Diversity",
"No training in these areas",
"Personal Development",
"Professional Development",
"Technical Training",
"Wellbeing and Support"))
gridExtra::grid.arrange(p1,
p2,
ncol = 2,
top = "I received training in these areas",
widths = c(1,3))
student_survey_responses_q8a_clean <- na.omit(data.frame("Year" = as.factor(student_survey_responses[,6]),
"Q8a" = as.factor(student_survey_responses[,22] )))
Q8a = student_survey_responses_q8a_clean %>% separate(Q8a, into = paste0(1:8), sep = ";") # separate column of responses
Q8b[Q8b == ""] <- NA # set empty values as NA
# wide to long
qawide = gather(Q8a, Rank, Answer, 2:9)
p1 = ggplot(qawide, mapping = aes(fill = as.factor(Answer), x = as.factor(Rank))) + geom_bar(color =
"black") +
labs(title = "Overall", y = "Count", fill = "Answer") + scale_fill_brewer(palette = "Set3") + theme_classic() + theme(
legend.position = "none",
axis.title.x = element_blank(),
title = element_text(size = 8)
) +
geom_text(
stat = 'count',
aes(label = ..count..),
position = position_stack(vjust = 0.5),
size = 2
)
p2 = ggplot(qawide, mapping = aes(fill = as.factor(Answer), x = as.factor(Rank))) + geom_bar(color =
"black") + facet_wrap( ~ Year) + # create a different plot for each Year
labs(y = "Count", fill = "Answer") + theme_classic() + theme(
legend.position = "right",
axis.title.x = element_blank(),
legend.text = element_text(size = 7)
) +
geom_text(
stat = 'count',
aes(label = ..count..),
position = position_stack(vjust = 0.5),
size = 2
) + scale_fill_brewer(
labels = c(
"Career Development",
"Cohort Specifc Training",
"Engagement and Outreach",
"Equality and Diversity",
"Personal Development",
"Professional Development",
"Technical Training",
"Wellbeing and Support"
),
palette = "Set3"
)
gridExtra::grid.arrange(
p1,
p2,
ncol = 2,
top = "I found training in these areas most helpful:",
bottom = "Most to least prefered mode of training",
widths = c(1, 3)
)
plot_q8_full <- ggplot(subset(dat_studentupdated, !is.na(equal_diversity)), aes(x=equal_diversity, fill = equal_diversity)) +
geom_bar(color = "black") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank()) +
labs(y = "Count") +
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,18)
plot_q8 <- ggplot(subset(dat_studentupdated, !is.na(equal_diversity)), aes(x=equal_diversity, fill = equal_diversity)) +
geom_bar(color = "black") +
facet_wrap(~yearofstudy) +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
labs(y = "Count")+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,7)
p_table <- subset(dat_studentupdated, !is.na(equal_diversity)) %>%
group_by(equal_diversity,yearofstudy) %>%
summarise(count=n()) %>%
gridExtra::tableGrob(.,cols = c("Usefulness", "Year", "Count"), rows = NULL)
gridExtra::grid.arrange(plot_q8_full,
plot_q8,
ncol = 2,
top = "Usefulness of equality and diversity training")
plot_q9_full <- ggplot(subset(dat_studentupdated, !is.na(professional_dev)),
aes(x=professional_dev, fill = professional_dev)) +
geom_bar(color = "black") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank()) +
labs(y = "Count")+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,15)
plot_q9 <- ggplot(subset(dat_studentupdated, !is.na(professional_dev)),
aes(x=professional_dev, fill = professional_dev)) +
geom_bar(color = "black") +
facet_wrap(~yearofstudy) +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
labs(y = "Count")+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,9)
gridExtra::grid.arrange(plot_q9_full,
plot_q9,
ncol = 2,
top = "Usefulness of professional development training")
plot_q10_full <- ggplot(subset(dat_studentupdated, !is.na(personal_dev)),
aes(x=personal_dev, fill = personal_dev),) +
geom_bar(color = "black") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank()) +
labs(y = "Count") +
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,13)
plot_q10 <- ggplot(subset(dat_studentupdated, !is.na(personal_dev)),
aes(x=personal_dev, fill = personal_dev)) +
geom_bar(color = "black") +
facet_wrap(~yearofstudy) +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
labs(y = "Count")+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,6)
gridExtra::grid.arrange(plot_q10_full,
plot_q10,
ncol = 2,
top = "Usefulness of personal development training")
plot_q11_full <- ggplot(subset(dat_studentupdated, !is.na(wellbeing)),
aes(x=wellbeing, fill = wellbeing)) +
geom_bar(position ='dodge', color = "black") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank()) +
labs(y = "Count")+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,15)
plot_q11 <- ggplot(subset(dat_studentupdated, !is.na(wellbeing)),
aes(x=wellbeing, fill = wellbeing)) +
geom_bar(position ='dodge',color = "black") +
facet_wrap(~yearofstudy) +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
labs(y = "Count")+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,8)
gridExtra::grid.arrange(plot_q11_full,
plot_q11,
ncol = 2,
top = "Usefulness of personal development training")
plot_q12_full <- ggplot(subset(dat_studentupdated, !is.na(career_dev)),
aes(x=career_dev, fill = career_dev)) +
geom_bar(color = "black") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank()) +
labs(y = "Count")+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,18)
plot_q12 <- ggplot(subset(dat_studentupdated, !is.na(career_dev)),
aes(x=career_dev, fill = career_dev)) +
geom_bar(position ='dodge',color = "black") +
facet_wrap(~yearofstudy) +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
labs(y = "Count")+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,8)
gridExtra::grid.arrange(plot_q12_full,
plot_q12,
ncol = 2,
top = "Usefulness of career development training")
plot_q13_full <- ggplot(subset(dat_studentupdated, !is.na(tech_training)),
aes(x=tech_training, fill = tech_training)) +
geom_bar(position ='dodge', color = "black") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank()) +
labs(y = "Count")+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,17)
plot_q13 <- ggplot(subset(dat_studentupdated, !is.na(tech_training)),
aes(x=tech_training, fill = tech_training)) +
geom_bar(position ='dodge',color = "black") +
facet_wrap(~yearofstudy) +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
labs(y = "Count")+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,8)
gridExtra::grid.arrange(plot_q13_full,
plot_q13,
ncol = 2,
top = "Usefulness of technical training")
plot_q14_full <- ggplot(subset(dat_studentupdated, !is.na(cohort_training)),
aes(x=cohort_training, fill = cohort_training)) +
geom_bar(color = "black") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank()) +
labs(y = "Count")+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,15)
plot_q14 <- ggplot(subset(dat_studentupdated, !is.na(cohort_training)),
aes(x=cohort_training, fill = cohort_training)) +
geom_bar(color = "black") +
facet_wrap(~yearofstudy) +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
labs(y = "Count")+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,7)
gridExtra::grid.arrange(plot_q14_full,
plot_q14,
ncol = 2,
top = "Usefulness of cohort-specific training")
plot_q15_full <- ggplot(subset(dat_studentupdated, !is.na(engage_outreach)),
aes(x=engage_outreach, fill = engage_outreach)) +
geom_bar(color = "black") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank()) +
labs(y = "Count")+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,14)
plot_q15 <- ggplot(subset(dat_studentupdated, !is.na(engage_outreach)),
aes(x=engage_outreach, fill = engage_outreach)) +
geom_bar(position ='dodge',color = "black") +
facet_wrap(~yearofstudy) +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
labs(y = "Count")+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,7)
gridExtra::grid.arrange(plot_q15_full,
plot_q15,
ncol = 2,
top = "Usefulness of engaging and outreach training")
Data for the following question was looked at and grouped into the following variable manually due to its free-text nature (Distribution of answers : NA / Yes (including detailed comments) / No)
Out of the “Yes” answers, n=2 were just “Yes” and n=11 gave detailed answers. Out of these 11 answers : - 3 mentioned issues related to their well being, including struggles with “loneliness” and “self-doubt” - 6 mentioned issues related to academia / PhD-specific, including struggles with selecting a supervisor, project management and grant applications - 4 mentioned issues related to their career beyond research skills, including struggles with networking, planning post their PhD and managing their finances (2) (Some answers included multiple different points)
student_survey_responses_q18 <- data.frame("Year" = as.factor(student_survey_responses[,6]),
"Q18" = as.factor(student_survey_responses[,23] ))
student_survey_responses_q18_clean <- data.frame("Q18" = c(rep("Not answered",20), rep("Yes",2), rep("No",3), rep("Yes", 11)),
"Year" = c(rep("Year 1", 4), rep("Year 2", 9), rep("Year 3", 6), rep("Year 4", 1),
rep("Year 1", 1), rep("Year 2", 1), rep("Year 2", 2), rep("Year 3", 1),
rep("Year 1", 4), rep("Year 2", 3), rep("Year 3", 4)))
student_survey_responses_q18_clean$Q18 <- factor(student_survey_responses_q18_clean$Q18, levels = c("Yes", "No", "Not answered")) #reorder the levels of Q7 in the way we want to present them "
plot_q18_full <- ggplot(data = student_survey_responses_q18_clean,
mapping = aes (x = Q18, fill = Q18)) +
geom_bar(color= "black") +
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,22)
plot_q18 <- ggplot(data = student_survey_responses_q18_clean,
mapping = aes (x = Q18, fill = Q18)) +
geom_bar(color= "black") + #barcharts
facet_wrap( ~ Year) + # create a different plot for each Year
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,11)
q18_table = student_survey_responses_q18_clean %>% group_by(Year, Q18) %>% summarise(count =
n())
gridExtra::grid.arrange(plot_q18_full,
plot_q18,
ncol = 2,
top = "Are there any aspects of the PhD project or \nlife as a PhD student you do not feel equipped to manage? (n = 36)")
Format of training
Key points:
Subject specific training was most useful for people in Year 2
Project management was most useful to students in Year 3
The format of training was judged appropriate by most
Training was mostly reported to be compulsory
Training was considered a good use of time and resources
student_open <- as.data.frame(read_xlsx("student_open.xlsx"))
student_open$Year = student_open[,2]
plot_q19 <- ggplot(data = subset(student_open, !is.na(training_like)),
mapping = aes (x = training_like, fill = training_like)) +
geom_bar(color = "black") + #barcharts
facet_wrap(~ Year) + # create a different plot for each Year
labs(x = "Answer", y = "Count", fill = "Answer") +
theme(axis.text.x = element_blank())+
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,6)
plot_q19_full <- ggplot(data = subset(student_open, !is.na(training_like)),
mapping = aes (x = training_like, fill = training_like)) +
geom_bar(color = "black") +
labs(x = "Answer", y = "Count", fill = "Answer") +
theme(axis.text.x = element_blank())+
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,9)
gridExtra::grid.arrange(plot_q19_full,
plot_q19,
ncol = 2,
top = "What training would you like to be offered?")
student_survey_responses_q21_clean <- na.omit(data.frame("Year" = as.factor(student_survey_responses[,6]),
"Q22" = as.factor(student_survey_responses[,26] )))
n_q21 <- length(student_survey_responses_q21_clean[,1]) #get the sample size for Q21
mylist = unlist(sapply(as.character(student_survey_responses_q21_clean[,2]), strsplit,split =';', USE.NAMES = F)) # get all responses
Val =c(levels(as.factor(mylist))) # get unique responses
df1 = student_survey_responses_q21_clean %>% filter(Year == 'Year 1') ;df2 = student_survey_responses_q21_clean %>% filter(Year == 'Year 2') ; df3 = student_survey_responses_q21_clean %>% filter(Year == 'Year 3')
Var = list(df1,df2,df3) # create list of dfs.
names(Var) <- c('Year1','Year2','Year3') # name the data frames for each year responses
cat("pref","mode","Year",file="Testfile.txt",sep=",",fill = TRUE) ##create a log file to append indices for each response
Ind = function(x){
for (i in 1:length(Var)) {
year = (names(Var)[i])
LIST = sapply(as.character(Var[[i]][[2]]), strsplit,split =';', USE.NAMES = F)
for (l in LIST) {
cat(which( l == x),x,year,file="Testfile.txt",sep = ",",fill = TRUE, append = TRUE) # write data to log fie
}
}
}
Val = sapply(Val,Ind,USE.NAMES = F)
plot_q22 = read.table('Testfile.txt',header = T, sep = ',')
p1 = ggplot(plot_q22, mapping = aes(fill = as.factor(mode),x = as.factor(pref))) + geom_bar(color="black") + # create a different plot for each Year
labs(title = "Overall", y = "Count", fill = "Answer") + scale_fill_brewer(palette = "Set3") + theme_classic() + theme(legend.position = "none", axis.title.x = element_blank(), title=element_text(size=8)) +
geom_text(stat='count', aes(label=..count..), position = position_stack(vjust = 0.5), size = 2)
p2 = ggplot(plot_q22, mapping = aes(fill = as.factor(mode),x = as.factor(pref))) + geom_bar(color="black") + facet_wrap(~ Year) + # create a different plot for each Year
labs( y = "Count", fill = "Answer") + scale_fill_brewer(palette = "Set3") + theme_classic() + theme(legend.position = "right", axis.title.x = element_blank(), legend.text = element_text(size=7))+
geom_text(stat='count', aes(label=..count..), position = position_stack(vjust = 0.5), size = 2)
gridExtra::grid.arrange(p1,
p2,
ncol = 2,
top = "I most enjoyed training delivered as (n = 31)",
bottom = "Most to least prefered mode of training",
widths = c(1,3))
student_survey_responses_q22_clean <- na.omit(data.frame("Year" = as.factor(student_survey_responses[,6]),
"Q22" = as.factor(student_survey_responses[,27] ))) #create a dataframe with Q1 and Q22 and remove NA values in answers to Q22
n_q22 <- length(student_survey_responses_q22_clean[,1]) #get the sample size for Q22
student_survey_responses_q22_clean$Q22 <- factor(student_survey_responses_q22_clean$Q22, levels = c("Strongly agree", "Agree", "Disagree", "Strongly disagree", "Not applicable")) #reorder the levels of Q22 in the way we want to present them
plot_q22_full <- ggplot(data = student_survey_responses_q22_clean,
mapping = aes (x = Q22, fill = Q22)) +
geom_bar(color= "black") +
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,25)
plot_q22 <- ggplot(data = student_survey_responses_q22_clean,
mapping = aes (x = Q22, fill = Q22)) +
geom_bar(color= "black") + #barcharts
facet_wrap( ~ Year) + # create a different plot for each Year
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,11)
q22_table = student_survey_responses_q22_clean %>% group_by(Year, Q22) %>% summarise(count =
n())
gridExtra::grid.arrange(plot_q22_full,
plot_q22,
ncol = 2,
top = "I feel the format of the training is generally appropriate to \nthe subject matter (n = 33)")
student_survey_responses_q23_clean <- na.omit(data.frame("Year" = as.factor(student_survey_responses[,6]),
"Q23" = as.factor(student_survey_responses[,28] ))) #create a dataframe with Q1 and Q23 and remove NA values in answers to Q23
n_q23 <- length(student_survey_responses_q23_clean[,1]) #get the sample size for Q23
student_survey_responses_q23_clean$Q23 <- factor(student_survey_responses_q23_clean$Q23, levels = c("Strongly agree", "Agree", "Disagree", "Strongly disagree", "Not applicable")) #reorder the levels of Q23 in the way we want to present them
plot_q23_full <- ggplot(data = student_survey_responses_q23_clean,
mapping = aes (x = Q23, fill = Q23)) +
geom_bar(color= "black") +
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,25)
plot_q23 <- ggplot(data = student_survey_responses_q23_clean,
mapping = aes (x = Q23, fill = Q23)) +
geom_bar(color= "black") + #barcharts
facet_wrap( ~ Year) + # create a different plot for each Year
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,8)
q23_table = student_survey_responses_q23_clean %>% group_by(Year, Q23) %>% summarise(count =
n())
gridExtra::grid.arrange(plot_q23_full,
plot_q23,
ncol = 2,
top = "Training offered by my programme is compulsory (n = 33)")
student_survey_responses_q24_clean <- na.omit(data.frame("Year" = as.factor(student_survey_responses[,6]),
"Q24" = as.factor(student_survey_responses[,29] ))) #create a dataframe with Q1 and Q24 and remove NA values in answers to Q24
n_q24 <- length(student_survey_responses_q24_clean[,1]) #get the sample size for Q24
student_survey_responses_q24_clean$Q24 <- factor(student_survey_responses_q24_clean$Q24, levels = c("Strongly agree", "Agree", "Disagree", "Strongly disagree", "Not applicable")) #reorder the levels of Q24 in the way we want to present them
plot_q24_full <- ggplot(data = student_survey_responses_q24_clean,
mapping = aes (x = Q24, fill = Q24)) +
geom_bar(color= "black") +
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,25)
plot_q24 <- ggplot(data = student_survey_responses_q24_clean,
mapping = aes (x = Q24, fill = Q24)) +
geom_bar(color= "black") + #barcharts
facet_wrap( ~ Year) + # create a different plot for each Year
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,10)
q24_table = student_survey_responses_q24_clean %>% group_by(Year, Q24) %>% summarise(count =
n())
gridExtra::grid.arrange(plot_q24_full,
plot_q24,
ncol = 2,
top = "Training offered by my programme is compulsory (n = 33)")
Usefulness of training
Subject specific training was reported to be the most useful
Most students reported using skills acquired through personal development training during the course of their PhD, although some did not
plot_q25b <- ggplot(data = subset(student_open, !is.na(training_useful)),
mapping = aes (x = training_useful, fill = training_useful)) +
geom_bar(color = "black") + #barcharts
facet_wrap(~ Year) + # create a different plot for each Year
labs(x = "Answer", y = "Count", fill = "Answer") +
theme(axis.text.x = element_blank())+
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank()) +
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,7)
plot_q25b_full <- ggplot(data = subset(student_open, !is.na(training_useful)),
mapping = aes (x = training_useful, fill = training_useful)) +
geom_bar(color = "black") +
labs(x = "Answer", y = "Count", fill = "Answer") +
theme(axis.text.x = element_blank())+
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,25)
gridExtra::grid.arrange(plot_q25b_full,
plot_q25b,
ncol = 2,
top = "What training modules did you find most useful?")
student_survey_responses_q26_clean <- na.omit(data.frame("Year" = as.factor(student_survey_responses[,6]),
"Q26" = as.factor(student_survey_responses[,31] ))) #create a dataframe with Q1 and Q26 and remove NA values in answers to Q26
n_q26 <- length(student_survey_responses_q26_clean[,1]) #get the sample size for Q26
student_survey_responses_q26_clean$Q26 <- factor(student_survey_responses_q26_clean$Q26, levels = c("Yes", "No")) #reorder the levels of Q26 in the way we want to present them "
plot_q26_full <- ggplot(data = student_survey_responses_q26_clean,
mapping = aes (x = Q26, fill = Q26)) +
geom_bar(color= "black") +
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank()) +
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,25)
plot_q26 <- ggplot(data = student_survey_responses_q26_clean,
mapping = aes (x = Q26, fill = Q26)) +
geom_bar(color= "black") + #barcharts
facet_wrap( ~ Year) + # create a different plot for each Year
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,9)
q26_table = student_survey_responses_q26_clean %>% group_by(Year, Q26) %>% summarise(count =
n())
gridExtra::grid.arrange(plot_q26_full,
plot_q26,
ncol = 2,
top = "I have used skills learned in personal development training during \nthe course of my PhD so far (n = 32)")
Process of recording training
Key points:
Universities kept records of training
Students know how to access the records
student_survey_responses_q27_clean <- na.omit(data.frame("Year" = as.factor(student_survey_responses[,6]),
"Q27" = as.factor(student_survey_responses[,32] ))) #create a dataframe with Q1 and Q27 and remove NA values in answers to Q27
n_q27 <- length(student_survey_responses_q27_clean[,1]) #get the sample size for Q27
student_survey_responses_q27_clean$Q27 <- factor(student_survey_responses_q27_clean$Q27, levels = c("Yes", "No")) #reorder the levels of Q27 in the way we want to present them "
plot_q27_full <- ggplot(data = student_survey_responses_q27_clean,
mapping = aes (x = Q27, fill = Q27)) +
geom_bar(color= "black") +
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,28)
plot_q27 <- ggplot(data = student_survey_responses_q27_clean,
mapping = aes (x = Q27, fill = Q27)) +
geom_bar(color= "black") + #barcharts
facet_wrap( ~ Year) + # create a different plot for each Year
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,10)
q27_table = student_survey_responses_q27_clean %>% group_by(Year, Q27) %>% summarise(count =
n())
gridExtra::grid.arrange(plot_q27_full,
plot_q27,
ncol = 2,
top = "A record of my training is kept by my university (n = 32)")
student_survey_responses_q28_clean <- na.omit(data.frame("Year" = as.factor(student_survey_responses[,6]),
"Q28" = as.factor(student_survey_responses[,33] ))) #create a dataframe with Q1 and Q28 and remove NA values in answers to Q28
n_q28 <- length(student_survey_responses_q28_clean[,1]) #get the sample size for Q28
student_survey_responses_q28_clean$Q28 <- factor(student_survey_responses_q28_clean$Q28, levels = c("Yes", "No", "N/A - my university does not provide a way to record my training.")) #reorder the levels of Q28 in the way we want to present them "
plot_q28_full <- ggplot(data = student_survey_responses_q28_clean,
mapping = aes (x = Q28, fill = Q28)) +
geom_bar(color= "black") +
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank()) +
scale_x_discrete(labels=c("Yes" = "Yes", "No" = "No", "N/A - my university does not provide a way to record my training." = "Record not provided")) +
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,25)
plot_q28 <- ggplot(data = student_survey_responses_q28_clean,
mapping = aes (x = Q28, fill = Q28)) +
geom_bar(color= "black") + #barcharts
facet_wrap( ~ Year) + # create a different plot for each Year
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank()) +
scale_x_discrete(labels=c("Yes" = "Yes", "No" = "No", "N/A - my university does not provide a way to record my training." = "Record not provided"))+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,10)
q28_table = student_survey_responses_q28_clean %>% group_by(Year, Q28) %>% summarise(count =
n())
gridExtra::grid.arrange(plot_q28_full,
plot_q28,
ncol = 2,
top = "I know how to access and record training \nin the training log provided by my university (n = 32)")
Overall satisfaction
Key points:
Most students are satisfied with training and think it will prepare them to become good researchers
Not all students in their second year think skills will be transferable to outside of academia
Most students wanted to learn new skills relevant to their PhDs when engaging in training
student_survey_responses_q29_clean <- na.omit(data.frame("Year" = as.factor(student_survey_responses[,6]),
"Q29" = as.factor(student_survey_responses[,34] ))) #create a dataframe with Q1 and Q29 and remove NA values in answers to Q29
n_q29 <- length(student_survey_responses_q29_clean[,1]) #get the sample size for Q29
student_survey_responses_q29_clean$Q29 <- factor(student_survey_responses_q29_clean$Q29, levels = c("Strongly agree", "Agree", "Disagree", "Strongly disagree", "Not applicable")) #reorder the levels of Q29 in the way we want to present them
plot_q29_full <- ggplot(data = student_survey_responses_q29_clean,
mapping = aes (x = Q29, fill = Q29)) +
geom_bar(color= "black") +
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank()) +
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,25)
plot_q29 <- ggplot(data = student_survey_responses_q29_clean,
mapping = aes (x = Q29, fill = Q29)) +
geom_bar(color= "black") + #barcharts
facet_wrap( ~ Year) + # create a different plot for each Year
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,12)
q29_table = student_survey_responses_q29_clean %>% group_by(Year, Q29) %>% summarise(count =
n())
gridExtra::grid.arrange(plot_q29_full,
plot_q29,
ncol = 2,
top = "Training provided as part of my programme prepares me \nto become a good researcher (n = 32)")
student_survey_responses_q30_clean <- na.omit(data.frame("Year" = as.factor(student_survey_responses[,6]),
"Q30" = as.factor(student_survey_responses[,35] ))) #create a dataframe with Q1 and Q30 and remove NA values in answers to Q30
n_q30 <- length(student_survey_responses_q30_clean[,1]) #get the sample size for Q30
student_survey_responses_q30_clean$Q30 <- factor(student_survey_responses_q30_clean$Q30, levels = c("Strongly agree", "Agree", "Disagree", "Strongly disagree")) #reorder the levels of Q30 in the way we want to present them
plot_q30_full <- ggplot(data = student_survey_responses_q30_clean,
mapping = aes (x = Q30, fill = Q30)) +
geom_bar(color= "black") +
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Accent") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,20)
plot_q30 <- ggplot(data = student_survey_responses_q30_clean,
mapping = aes (x = Q30, fill = Q30)) +
geom_bar(color= "black") + #barcharts
facet_wrap( ~ Year) + # create a different plot for each Year
labs(
x = "Answer",
y = "Count",
fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,9)
q30_table = student_survey_responses_q30_clean %>% group_by(Year, Q30) %>% summarise(count =
n())
gridExtra::grid.arrange(plot_q30_full,
plot_q30,
ncol = 2,
top = "Training provided as part of my programme gives me valuable \ntransferable skills for working outside of academia (n = 32)")
For these last two open question, comments were read, common themes were detected and comments were assigned to categories.
plot_q31b <- ggplot(data = subset(student_open, !is.na(goals_training)),
mapping = aes (x = goals_training, fill = goals_training)) +
geom_bar(color = "black") + #barcharts
facet_wrap(~ Year) + # create a different plot for each Year
labs(x = "Answer", y = "Count", fill = "Answer") +
theme(axis.text.x = element_blank())+
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,9)
plot_q31b_full <- ggplot(data = subset(student_open, !is.na(goals_training)),
mapping = aes (x = goals_training, fill = goals_training)) +
geom_bar(color = "black") +
labs(x = "Answer", y = "Count", fill = "Answer") +
theme(axis.text.x = element_blank())+
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "none",axis.title.x = element_blank())+
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,25)
gridExtra::grid.arrange(plot_q31b_full,
plot_q31b,
ncol = 2,
top = "What was your main objective when engaging with training?")
“in person” = prefferred training to be delivered in person in the future
“share courses” = course information should be shared across Wellcome programmes
“good but missing” = training was good but lacked courses on some topics
plot_q32 <- ggplot(data = subset(student_open, !is.na(comments)),
mapping = aes (x = comments, fill = comments)) +
geom_bar(color = "black") + #barcharts + # create a different plot for each Year
labs(title = "General comments on training?" , x = "Answer", y = "Count", fill = "Answer") +
scale_fill_brewer(palette = "Paired") + theme_classic() +
theme(axis.text.x = element_text(angle = 90), legend.position = "Accent",axis.title.x = element_blank()) +
geom_text(stat='count', aes(label=..count..), vjust=-1) + ylim(0,5)
plot_q32
Summarise and save data
summary = lapply(student_survey_responses[-c(1:5)], table)
save(summary, file = "summary_tally.RData")